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<a class="small-title" href="paper_pages/heeJqQXKg7.html">LitLLMs, LLMs for Literature Review: Are we there yet?</a>
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<div class="author-str">Shubham Agarwal · Gaurav Sahu · Abhay Puri · Issam H. Laradji · Krishnamurthy Dj Dvijotham · Jason Stanley · Laurent Charlin · Christopher Pal</div>
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<p>Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially due to the recent influx of research papers. This paper explores the zero-shot abilities of recent Large Language Models (LLMs) in assisting with the writing of literature reviews based on an abstract. We decompose the task into two components: (1) Retrieving related works given a query abstract and (2) Writing a literature review based on the retrieved results. We analyze how effective LLMs are for both components. For retrieval, we introduce a novel two-step search strategy that first uses an LLM to extract meaningful keywords from the abstract of a paper and then retrieves potentially relevant papers by querying an external knowledge base. Additionally, we study a prompting-based re-ranking mechanism with attribution and show that re-ranking doubles the normalized recall compared to naive search methods while providing insights into the LLM’s decision-making process. In the generation phase, we propose a two-step approach that first outlines a plan for the review and then executes steps in the plan to generate the actual review. To evaluate different LLM-based literature review methods, we create test sets from arXiv papers using a protocol designed for rolling use with newly released LLMs to avoid test set contamination in zero-shot evaluations. We release this evaluation protocol to promote additional research and development in this regard. Our empirical results suggest that LLMs show promising potential for writing literature reviews when the task is decomposed into smaller components of retrieval and planning. Particularly, we find that combining keyword-based and document-embedding-based search improves precision and recall during retrieval by 10% and 30%, respectively, compared to using either of the methods in isolation. Further, we demonstrate that our planning-based approach achieves higher-quality reviews by minimizing hallucinated references in the generated review by 18-26% compared to existing simpler LLM-based generation methods. Our project page including a demonstration system and toolkit can be accessed here: https://litllm.github.io.</p>
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<a class="small-title" href="paper_pages/k3Ab6RuJE9.html">Verbalized Machine Learning: Revisiting Machine Learning with Language Models</a>
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<div class="author-str">Tim Z. Xiao · Robert Bamler · Bernhard Schölkopf · Weiyang Liu</div>
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<p>Motivated by the progress of large language models (LLMs), we introduce the framework of verbalized machine learning (VML). In contrast to conventional machine learning (ML) models that are typically optimized over a continuous parameter space, VML constrains the parameter space to be human-interpretable natural language. Such a constraint leads to a new perspective of function approximation, where an LLM with a text prompt can be viewed as a function parameterized by the text prompt. Guided by this perspective, we revisit classical ML problems, such as regression and classification, and find that these problems can be solved by an LLM-parameterized learner and optimizer. The major advantages of VML include (1) easy encoding of inductive bias: prior knowledge about the problem and hypothesis class can be encoded in natural language and fed into the LLM-parameterized learner; (2) automatic model class selection: the optimizer can automatically select a model class based on data and verbalized prior knowledge, and it can update the model class during training; and (3) interpretable learner updates: the LLM-parameterized optimizer can provide explanations for why an update is performed. We empirically verify the effectiveness of VML, and hope that VML can serve as a stepping stone to stronger interpretability.</p>
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<a class="small-title" href="paper_pages/sSOxuUjE2o.html">Controlled Training Data Generation with Diffusion Models</a>
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<div class="author-str">Teresa Yeo · Andrei Atanov · Harold Luc Benoit · Aleksandr Alekseev · Ruchira Ray · Pooya Esmaeil Akhoondi · Amir Zamir</div>
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<p>We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach via pre-defined prompts to generate new data using either a language model or human expertise, we develop an automated closed-loop system that involves two feedback mechanisms. The first mechanism uses feedback from a given supervised model to find adversarial prompts that result in generated images that maximize the model's loss and, consequently, expose its vulnerabilities. While these adversarial prompts generate training examples curated for improving the given model, they are not curated for a specific target distribution of interest, which can be inefficient. Therefore, we introduce the second feedback mechanism that can optionally guide the generation process towards a desirable target distribution. We call the method combining these two mechanisms Guided Adversarial Prompts. The proposed closed-loop system allows us to control the training data generation for a given model and target image distribution. We evaluate on different tasks, datasets, and architectures, with different types of distribution shifts (corruptions, spurious correlations, unseen domains) and illustrate the advantages of the proposed feedback mechanisms compared to open-loop approaches.</p>
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<a class="small-title" href="paper_pages/0AOUWC4ss8.html">Illustrated Landmark Graphs for Long-horizon Policy Learning</a>
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<div class="author-str">Christopher Watson · Arjun Krishna · Rajeev Alur · Dinesh Jayaraman</div>
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<p>Applying learning-based approaches to long-horizon sequential decision-making tasks requires a human teacher to carefully craft reward functions or curate demonstrations to elicit desired behaviors. To simplify this, we first introduce an alternative form of task-specification, Illustrated Landmark Graph (ILG), that represents the task as a directed graph where each vertex corresponds to a region of the state space (a landmark), and each edge represents an easier to achieve sub-task. A landmark in the ILG is conveyed to the agent through a few illustrative examples grounded in the agent’s observation space. Second, we propose ILG-Learn, a human in the loop algorithm that interleaves planning over the ILG and sub-task policy learning. ILG-Learn adaptively plans through the ILG by relying on the human teacher’s feedback to estimate the success rates of learned policies. We conduct experiments on long-horizon block stacking and point maze navigation tasks, and find that our approach achieves considerably higher success rates (~ 50% improvement) compared to hierarchical reinforcement learning and imitation learning baselines. Additionally, we highlight how the flexibility of the ILG specification allows the agent to learn a sequence of sub-tasks that is better suited to its limited capabilities.</p>
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<a class="small-title" href="paper_pages/EoiuRII7MQ.html">Lower Ricci Curvature for Efficient Community Detection</a>
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<div class="author-str">Yun Jin Park · Didong Li</div>
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<p>This study introduces the Lower Ricci Curvature (LRC), a novel, scalable, and scale-free discrete curvature designed to enhance community detection in networks. Addressing the computational challenges posed by existing curvature-based methods, LRC offers a streamlined approach with linear computational complexity, which makes it well suited for large-scale network analysis. We further develop an LRC-based preprocessing method that effectively augments popular community detection algorithms. Through applications on multiple real-world datasets, including the NCAA football league network, the DBLP collaboration network, the Amazon product co-purchasing network, and the YouTube social network, we demonstrate the efficacy of our method in significantly improving the performance of various community detection algorithms.</p>
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<a class="small-title" href="paper_pages/s1zfBJysbI.html">Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting</a>
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<div class="author-str">Inkyu Shin · Qihang Yu · Xiaohui Shen · In So Kweon · Kuk-Jin Yoon · Liang-Chieh Chen</div>
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<p>Recent advancements in zero-shot video diffusion models have shown promise for text-driven video editing, but challenges remain in achieving high temporal consistency. To address this, we introduce Video-3DGS, a 3D Gaussian Splatting (3DGS)-based video refiner designed to enhance temporal consistency in zero-shot video editors. Our approach utilizes a two-stage 3D Gaussian optimizing process tailored for editing dynamic monocular videos. In the first stage, Video-3DGS employs an improved version of COLMAP, referred to as MC-COLMAP, which processes original videos using a Masked and Clipped approach. For each video clip, MC-COLMAP generates the point clouds for dynamic foreground objects and complex backgrounds. These point clouds are utilized to initialize two sets of 3D Gaussians (Frg-3DGS and Bkg-3DGS) aiming to represent foreground and background views. Both foreground and background views are then merged with a 2D learnable parameter map to reconstruct full views. In the second stage, we leverage the reconstruction ability developed in the first stage to impose the temporal constraints on the video diffusion model. This approach ensures the temporal consistency in the edited videos while maintaining high fidelity to the editing text prompt. We further propose a recursive and ensembled refinement by revisiting the denoising step and guidance scale used in video diffusion process with Video-3DGS. To demonstrate the efficacy of Video-3DGS on both stages, we conduct extensive experiments across two related tasks: Video Reconstruction and Video Editing. Video-3DGS trained with 3k iterations significantly improves video reconstruction quality (+3 PSNR, +7 PSNR increase) and training efficiency (×1.9, ×4.5 times faster) over NeRF-based and 3DGS-based state-of-art methods on DAVIS dataset, respectively. Moreover, it enhances video editing by ensuring temporal consistency across 58 dynamic monocular videos.</p>
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<a class="small-title" href="paper_pages/OGifiton47.html">Active Diffusion Subsampling</a>
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<div class="author-str">Oisín Nolan · Tristan Stevens · Wessel L. van Nierop · Ruud Van Sloun</div>
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<p>Subsampling is commonly used to mitigate costs associated with data acquisition, such as
time or energy requirements, motivating the development of algorithms for estimating the
fully-sampled signal of interest $x$ from partially observed measurements $y$. In maximum-
entropy sampling, one selects measurement locations that are expected to have the highest
entropy, so as to minimize uncertainty about $x$. This approach relies on an accurate model
of the posterior distribution over future measurements, given the measurements observed so
far. Recently, diffusion models have been shown to produce high-quality posterior samples
of high-dimensional signals using guided diffusion. In this work, we propose Active Diffusion
Subsampling (ADS), a method for designing intelligent subsampling masks using guided dif-
fusion in which the model tracks a distribution of beliefs over the true state of $x$ throughout
the reverse diffusion process, progressively decreasing its uncertainty by actively choosing
to acquire measurements with maximum expected entropy, ultimately producing the pos-
terior distribution $p(x | y)$. ADS can be applied using pre-trained diffusion models for any
subsampling rate, and does not require task-specific retraining – just the specification of
a measurement model. Furthermore, the maximum entropy sampling policy employed by
ADS is interpretable, enhancing transparency relative to existing methods using black-box
policies. Experimentally, we show that through designing informative subsampling masks,
ADS significantly improves reconstruction quality compared to fixed sampling strategies on
the MNIST and CelebA datasets, as measured by standard image quality metrics, includ-
ing PSNR, SSIM, and LPIPS. Furthermore, on the task of Magnetic Resonance Imaging
acceleration, we find that ADS performs competitively with existing supervised methods in
reconstruction quality while using a more interpretable acquisition scheme design procedure.
Code is available at https://active-diffusion-subsampling.github.io/.</p>
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<a class="small-title" href="paper_pages/jJOVpnNrEp.html">Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics</a>
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<div class="author-str">Minttu Alakuijala · Reginald McLean · Isaac Woungang · Nariman Farsad · Samuel Kaski · Pekka Marttinen · Kai Yuan</div>
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<p>Natural language is often the easiest and most convenient modality for humans to specify tasks for robots. However, learning to ground language to behavior typically requires impractical amounts of diverse, language-annotated demonstrations collected on each target robot. In this work, we aim to separate the problem of what to accomplish from how to accomplish it, as the former can benefit from substantial amounts of external observation-only data, and only the latter depends on a specific robot embodiment. To this end, we propose Video-Language Critic, a reward model that can be trained on readily available cross-embodiment data using contrastive learning and a temporal ranking objective, and use it to score behavior traces from a separate actor. When trained on Open X-Embodiment data, our reward model enables 2x more sample-efficient policy training on Meta-World tasks than a sparse reward only, despite a significant domain gap. Using in-domain data but in a challenging task generalization setting on Meta-World, we further demonstrate more sample-efficient training than is possible with prior language-conditioned reward models that are either trained with binary classification, use static images, or do not leverage the temporal information present in video data.</p>
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<a class="small-title" href="paper_pages/HkmymFPODz.html">Deep Active Learning in the Open World</a>
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<div class="author-str">Tian Xie · Jifan Zhang · Haoyue Bai · Robert D Nowak</div>
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<p>Machine learning models deployed in open-world scenarios often encounter unfamiliar conditions and perform poorly in unanticipated situations. As AI systems advance and find application in safety-critical domains, effectively handling out-of-distribution (OOD) data is crucial to building open-world learning systems. In this work, we introduce ALOE, a novel active learning algorithm for open-world environments designed to enhance model adaptation by incorporating new OOD classes via a two-stage approach. First, diversity sampling selects a representative set of examples, followed by energy-based OOD detection to prioritize likely unknown classes for annotation. This strategy accelerates class discovery and learning, even under constrained annotation budgets. Evaluations on three long-tailed image classification benchmarks demonstrate that ALOE outperforms traditional active learning baselines, effectively expanding known categories while balancing annotation cost. Our findings reveal a crucial tradeoff between enhancing known-class performance and discovering new classes, setting the stage for future advancements in open-world machine learning.</p>
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<a class="small-title" href="paper_pages/IrBYuh9W3T.html">What Makes ImageNet Look Unlike LAION</a>
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<div class="author-str">Ali Shirali · Moritz Hardt</div>
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<p>ImageNet was famously created by querying several image search engines such as Flickr. What if we recreated ImageNet instead by searching the massive LAION dataset based on image captions alone? In this work, we carry out this counterfactual investigation. We find that the resulting ImageNet recreation, which we call LAIONet, looks distinctly unlike the original. Specifically, the intra-class similarity of images in the original ImageNet is dramatically higher than it is for LAIONet. Consequently, models trained on ImageNet perform significantly worse on LAIONet. We propose a rigorous explanation for the discrepancy in terms of a subtle, yet important, difference in two plausible causal data-generating processes for the respective datasets, that we support with systematic experimentation. In a nutshell, searching based on an image caption alone creates an information bottleneck that mitigates the selection bias otherwise present in image-based filtering. Our explanation formalizes a long-held intuition in the community that ImageNet images are stereotypical, unnatural, and overly simple representations of the class category. At the same time, it provides a simple and actionable takeaway for future dataset creation efforts.</p>
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<a class="small-title" href="paper_pages/Okxp1W8If0.html">(Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentum</a>
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<div class="author-str">Anh Quang Dang · Reza Babanezhad Harikandeh · Sharan Vaswani</div>
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<p>Stochastic heavy ball momentum (SHB) is commonly used to train machine learning models, and often provides empirical improvements over stochastic gradient descent. By primarily focusing on strongly-convex quadratics, we aim to better understand the theoretical advantage of SHB and subsequently improve the method. For strongly-convex quadratics, Kidambi et al. (2018) show that SHB (with a mini-batch of size $1$) cannot attain accelerated convergence, and hence has no theoretical benefit over SGD. They conjecture that the practical gain of SHB is a by-product of using larger mini-batches. We first substantiate this claim by showing that SHB can attain an accelerated rate when the mini-batch size is larger than a threshold $b^*$ that depends on the condition number $\kappa$. Specifically, we prove that with the same step-size and momentum parameters as in the deterministic setting, SHB with a sufficiently large mini-batch size results in an $O\left(\exp(-\frac{T}{\sqrt{\kappa}}) + \sigma \right)$ convergence when measuring the distance to the optimal solution in the $\ell_2$ norm, where $T$ is the number of iterations and $\sigma^2$ is the variance in the stochastic gradients. We prove a lower-bound which demonstrates that a $\kappa$ dependence in $b^*$ is necessary. To ensure convergence to the minimizer, we design a noise-adaptive multi-stage algorithm that results in an $O\left(\exp\left(-\frac{T}{\sqrt{\kappa}}\right) + \frac{\sigma}{\sqrt{T}}\right)$ rate when measuring the distance to the optimal solution in the $\ell_2$ norm. We also consider the general smooth, strongly-convex setting and propose the first noise-adaptive SHB variant that converges to the minimizer at an $O(\exp(-\frac{T}{\kappa}) + \frac{\sigma^2}{T})$ rate when measuring the distance to the optimal solution in the squared $\ell_2$ norm. We empirically demonstrate the effectiveness of the proposed algorithms.</p>
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<div class="author-str">Ashka Shah · Adela Frances DePavia · Nathaniel C Hudson · Ian Foster · Rick Stevens</div>
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<p>The aim in many sciences is to understand the mechanisms that underlie the observed distribution of variables, starting from a set of initial hypotheses. Causal discovery allows us to infer mechanisms as sets of cause and effect relationships in a generalized way---without necessarily tailoring to a specific domain. Causal discovery algorithms search over a structured hypothesis space, defined by the set of Directed Acyclic Graphs (DAG), to find the graph that best explains the data. For high-dimensional problems, however, this search becomes intractable and scalable algorithms for causal discovery are needed to bridge the gap.
In this paper, we define a novel causal graph partition that allows for divide-and-conquer causal discovery with theoretical guarantees under the Maximal Ancestral Graph (MAG) class. We leverage the idea of a superstructure---a set of learned or existing candidate hypotheses---to partition the search space. We prove under certain assumptions that learning with a causal graph partition always yields the Markov Equivalence Class of the true causal graph. We show our algorithm achieves comparable accuracy and a faster time to solution for biologically-tuned synthetic networks and networks up to ${10^4}$ variables. This makes our method applicable to gene regulatory network inference and other domains with high-dimensional structured hypothesis spaces.</p>
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<a class="small-title" href="paper_pages/1QeI99nH9k.html">Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study</a>
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<div class="author-str">Cullen Anderson · Jeff M. Phillips</div>
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<p>Robust statistics aims to compute quantities to represent data where a fraction of it may be arbitrarily corrupted. The most essential statistic is the mean, and in recent years, there has been a flurry of theoretical advancement for efficiently estimating the mean in high dimensions on corrupted data. While several algorithms have been proposed that achieve near-optimal error, they all rely on large data size requirements as a function of dimension. In this paper, we perform an extensive experimentation over various mean estimation techniques where data size might not meet this requirement due to the high-dimensional setting.
For data with inliers generated from a Gaussian with known covariance, we find experimentally that several robust mean estimation techniques can practically improve upon the sample mean, with the quantum entropy scaling approach from Dong \etal (NeurIPS 2019) performing consistently the best. However, this consistent improvement is conditioned on a couple of simple modifications to how the steps to prune outliers work in the high-dimension low-data setting, and when the inliers deviate significantly from Gaussianity. In fact, with these modifications, they are typically able to achieve roughly the same error as taking the sample mean of the uncorrupted inlier data, even with very low data size. In addition to controlled experiments on synthetic data, we also explore these methods on large language models, deep pretrained image models, and non-contextual word embedding models that do not necessarily have an inherent Gaussian distribution. Yet, in these settings, a mean point of a set of embedded objects is a desirable quantity to learn, and the data exhibits the high-dimension low-data setting studied in this paper. We show both the challenges of achieving this goal, and that our updated robust mean estimation methods can provide significant improvement over using just the sample mean. We additionally publish a library of Python implementations of robust mean estimation algorithms, allowing practitioners and researchers to apply these techniques and to perform further experimentation.</p>
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<a class="small-title" href="paper_pages/aPyJilTiIb.html">A Unified View of Double-Weighting for Marginal Distribution Shift</a>
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<div class="author-str">José I. Segovia-Martín · Santiago Mazuelas · Anqi Liu</div>
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<p>Supervised classification traditionally assumes that training and testing samples are drawn from the same underlying distribution. However, practical scenarios are often affected by distribution shifts, such as covariate and label shifts. Most existing techniques for correcting distribution shifts are based on a reweighted approach that weights training samples, assigning lower relevance to the samples that are unlikely at testing. However, these methods may achieve poor performance when the weights obtained take large values at certain training samples. In addition, in multi-source cases, existing methods do not exploit complementary information among sources, and equally combine sources for all instances. In this paper, we establish a unified learning framework for distribution shift adaptation. We present a double-weighting approach to deal with distribution shifts, considering weight functions associated with both training and testing samples. For the multi-source case, the presented methods assign source-dependent weights for training and testing samples, where weights are obtained jointly using information from all sources. We also present generalization bounds for the proposed methods that show a significant increase in the effective sample size compared with existing approaches. Empirically, the proposed methods achieve enhanced classification performance in both synthetic and empirical experiments.</p>
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<a class="small-title" href="paper_pages/H4S4ETc8c9.html">Evaluation of Best-of-N Sampling Strategies for Language Model Alignment</a>
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<div class="author-str">Yuki Ichihara · Yuu Jinnai · Tetsuro Morimura · Kenshi Abe · Kaito Ariu · Mitsuki Sakamoto · Eiji Uchibe</div>
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<p>Best-of-N (BoN) sampling with a reward model has been shown to be an effective strategy for aligning Large Language Models (LLMs) with human preferences at the time of decoding. BoN sampling is susceptible to a problem known as reward hacking. Since the reward model is an imperfect proxy for the true objective, an excessive focus on optimizing its value can lead to a compromise of its performance on the true objective. Previous work proposes Regularized BoN sampling (RBoN), a BoN sampling with regularization to the objective, and shows that it outperforms BoN sampling so that it mitigates reward hacking and empirically (Jinnai et al., 2024). However, Jinnai et al. (2024) introduce RBoN based on a heuristic and they lack the analysis of why such regularization strategy improves the performance of BoN sampling. The aim of this study is to analyze the effect of BoN sampling on regularization strategies. Using the regularization strategies corresponds to robust optimization, which maximizes the worst case over a set of possible perturbations in the proxy reward. Although the theoretical guarantees are not directly applicable to RBoN, RBoN corresponds to a practical implementation. This paper proposes an extension of the RBoN framework, called Stochastic RBoN sampling (SRBoN), which is a theoretically guaranteed approach to worst-case RBoN in proxy reward. We then perform an empirical evaluation using the AlpacaFarm and Anthropic’s hh-rlhf datasets to evaluate which factors of the regularization strategies contribute to the improvement of the true proxy reward. In addition, we also propose another simple RBoN method, the Sentence Length Regularized BoN, which has a better performance in the experiment as compared to the previous methods.</p>
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<a class="small-title" href="paper_pages/O4CQ5AM5yP.html">REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation</a>
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<div class="author-str">Bas van der Heijden · Jens Kober · Robert Babuska · Laura Ferranti</div>
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<p>Sim2real, the transfer of control policies from simulation to the real world, is crucial for efficiently solving robotic tasks without the risks associated with real-world learning. However, discrepancies between simulated and real environments, especially due to unmodeled dynamics and latencies, significantly impact the performance of these transferred policies. In this paper, we address the challenges of sim2real transfer caused by latency and asynchronous dynamics in real-world robotic systems. Our approach involves developing a novel framework, REX (Robotic Environments with jaX), that uses a graph-based simulation model to incorporate latency effects while optimizing for parallelization on accelerator hardware. Our framework simulates the asynchronous, hierarchical nature of real-world systems, while simultaneously estimating system dynamics and delays from real-world data and implementing delay compensation strategies to minimize the sim2real gap. We validate our approach on two real-world systems, demonstrating its effectiveness in improving sim2real performance by accurately modeling both system dynamics and delays. Our results show that the proposed framework supports both accelerated simulation and real-time processing, making it valuable for robot learning.</p>
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<a class="small-title" href="paper_pages/HOnL5hjaIt.html">Generalized Tangent Kernel: A Unified Geometric Foundation for Natural Gradient and Standard Gradient</a>
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<div class="author-str">Qinxun Bai · Steven Rosenberg · Wei Xu</div>
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<p>Natural gradients have been widely studied from both theoretical and empirical perspectives, and it is commonly believed that natural gradients have advantages over standard (Euclidean) gradients in capturing the intrinsic geometric structure of the underlying function space and being invariant under reparameterization. However, for function optimization, a fundamental theoretical issue regarding the existence of natural gradients on the function space remains underexplored. We address this issue by providing a geometric perspective and mathematical framework for studying both natural gradient and standard gradient that is more complete than existing studies. The key tool that unifies natural gradient and standard gradient is a generalized form of the Neural Tangent Kernel (NTK), which we name the Generalized Tangent Kernel (GTK). Using a novel orthonormality property of GTK, we show that for a fixed parameterization, GTK determines a Riemannian metric on the entire function space which makes the standard gradient as “natural" as the natural gradient in capturing the intrinsic structure of the parameterized function space. Many aspects of this approach relate to RKHS theory. For the practical side of this theory paper, we showcase that our framework motivates new solutions to the non-immersion/degenerate case of natural gradient and leads to new families of natural/standard gradient descent methods.</p>
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<a class="small-title" href="paper_pages/ZRXwHRXm8i.html">CREW: Facilitating Human-AI Teaming Research</a>
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<div class="author-str">Lingyu Zhang · Zhengran Ji · Boyuan Chen</div>
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<p>With the increasing deployment of artificial intelligence (AI) technologies, the potential of humans working with AI agents has been growing at a great speed. Human-AI teaming is an important paradigm for studying various aspects when humans and AI agents work together. The unique aspect of Human-AI teaming research is the need to jointly study humans and AI agents, demanding multidisciplinary research efforts from machine learning to human-computer interaction, robotics, cognitive science, neuroscience, psychology, social science, and complex systems. However, existing platforms for Human-AI teaming research are limited, often supporting oversimplified scenarios and a single task, or specifically focusing on either human-teaming research or multi-agent AI algorithms. We introduce \textbf{CREW}, a platform to facilitate Human-AI teaming research in real-time decision-making scenarios and engage collaborations from multiple scientific disciplines, with a strong emphasis on human involvement. It includes pre-built tasks for cognitive studies and Human-AI teaming with expandable potentials from our modular design. Following conventional cognitive neuroscience research, CREW also supports multimodal human physiological signal recording for behavior analysis. Moreover, CREW benchmarks real-time human-guided reinforcement learning agents using state-of-the-art algorithms and well-tuned baselines. With CREW, we were able to conduct 50 human subject studies within a week to verify the effectiveness of our benchmark.</p>
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<a class="small-title" href="paper_pages/Utjw2z1ale.html">Identifying Spurious Correlations using Counterfactual Alignment</a>
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<div class="author-str">Joseph Paul Cohen · Louis Blankemeier · Akshay S Chaudhari</div>
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<p>Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual (CF) alignment method to detect and quantify spurious correlations of black box classifiers. Our methodology is based on counterfactual images generated with respect to one classifier being input into other classifiers to see if they also induce changes in the outputs of these classifiers. The relationship between these responses can be quantified and used to identify specific instances where a spurious correlation exists. This is validated by observing intuitive trends in face-attribute and waterbird classifiers, as well as by fabricating spurious correlations and detecting their presence, both visually and quantitatively. Furthermore, utilizing the CF alignment method, we demonstrate that we can evaluate robust optimization methods (GroupDRO, JTT, and FLAC) by detecting a reduction in spurious correlations.</p>
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<div class="author-str">João Bravo · Jacopo Bono · Hugo Ferreira · Pedro Saleiro · Pedro Bizarro</div>
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<p>Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). To manage the scale and complexity of these graph datasets, machine learning (ML) approaches have become essential. However, CTDGs pose challenges for ML because traditional static graph methods fail to account for event timings naturally. Newer approaches, such as graph recurrent neural networks (GRNNs), are inherently time-aware and offer advantages over static methods for CTDGs. Yet, GRNNs face another issue: the short truncation of backpropagation-through-time (BPTT) whose impact has never been properly examined until now. In this work, we demonstrate that this truncation can limit the learning of dependencies more than a hop away, resulting in reduced performance. Through experiments on a novel synthetic task as well as real-world datasets, we reveal that there exists a performance gap between full backpropagation-through-time (F-BPTT) and the truncated backpropagation-through-time (T-BPTT) commonly used to train GRNN models. We term this gap the "truncation gap" and argue that understanding and addressing it is essential as the importance of CTDGs grows, discussing potential future directions of research for this type of models.</p>
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<a class="small-title" href="paper_pages/7CUluLpLxV.html">Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors</a>
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<div class="author-str">Angus Nicolson · Lisa Schut · Alison Noble · Yarin Gal</div>
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<p>Concept-based explanations translate the internal representations of deep learning models into a language that humans are familiar with: concepts. One popular method for finding concepts is Concept Activation Vectors (CAVs), which are learnt using a probe dataset of concept exemplars. In this work, we investigate three properties of CAVs: (1) inconsistency across layers, (2) entanglement with other concepts, and (3) spatial dependency. Each property provides both challenges and opportunities in interpreting models. We introduce tools designed to detect the presence of these properties, provide insight into how each property can lead to misleading explanations, and provide recommendations to mitigate their impact. To demonstrate practical applications, we apply our recommendations to a melanoma classification task, showing how entanglement can lead to uninterpretable results and that the choice of negative probe set can have a substantial impact on the meaning of a CAV. Further, we show that understanding these properties can be used to our advantage. For example, we introduce spatially dependent CAVs to test if a model is translation invariant with respect to a specific concept and class. Our experiments are performed on natural images (ImageNet), skin lesions (ISIC 2019), and a new synthetic dataset, Elements. Elements is designed to capture a known ground truth relationship between concepts and classes. We release this dataset to facilitate further research in understanding and evaluating interpretability methods.</p>
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<a class="small-title" href="paper_pages/BsMMc4MEGS.html">CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark</a>
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<div class="author-str">Zachary S Siegel · Sayash Kapoor · Nitya Nadgir · Benedikt Stroebl · Arvind Narayanan</div>
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<p>AI agents have the potential to aid users on a variety of consequential tasks, including conducting scientific research. To spur the development of useful agents, we need benchmarks that are challenging, but more crucially, directly correspond to real-world tasks of interest. This paper introduces such a benchmark, designed to measure the accuracy of AI agents in tackling a crucial yet surprisingly challenging aspect of scientific research: computational reproducibility. This task, fundamental to the scientific process, involves reproducing the results of a study using the provided code and data. We introduce CORE-Bench (Computational Reproducibility Agent Benchmark), a benchmark consisting of 270 tasks based on 90 scientific papers across three disciplines (computer science, social science, and medicine). Tasks in CORE-Bench consist of three difficulty levels and include both language-only and vision-language tasks. We provide an evaluation system to measure the accuracy of agents in a fast and parallelizable way, saving days of evaluation time for each run compared to a sequential implementation. We evaluated two baseline agents: the general-purpose AutoGPT and a task-specific agent called CORE-Agent. We tested both variants using two underlying language models: GPT-4o and GPT-4o-mini. The best agent achieved an accuracy of 19% on the hardest level of tasks, showing the vast scope for improvement in automating routine scientific tasks. Having agents that can reproduce existing work is a necessary step toward building agents that can conduct novel research and could verify and improve the performance of other research agents. We hope that CORE-Bench can improve the state of reproducibility and spur the development of future research agents.</p>
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<a class="small-title" href="paper_pages/aWRMvXTvPf.html">Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions</a>
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<div class="author-str">Gabriel Dubé · Mario Marchand</div>
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<p>Shapley values are widely used in machine learning to interpret model predictions. However, they have an important drawback in their computational time, which is exponential in the number of variables in the data. Recent work has yielded algorithms that can efficiently and exactly calculate the Shapley values of specific model families, such as Decision Trees and Generalized Additive Models (GAMs). Unfortunately, these model families are fairly restricted.
Consequently, we present STAR-SHAP, an algorithm for efficiently calculating the Shapley values of Structured Additive Regression (STAR) models, a generalization of GAMs which allow any number of variable interactions. While the computational cost of STAR-SHAP scales exponentially in the size of these interactions, it is independent of the total number of variables. This allows the interpretation of more complex and flexible models. As long as the variable interactions are moderately-sized, the computation of the Shapley values will be fast, even on high-dimensional datasets.
Since STAR models with more than pairwise interactions (e.g. GA2Ms) are seldom used in practice, we also present a new class of STAR models built on the RKHS Weightings of Functions paradigm. More precisely, we introduce a new RKHS Weighting instantiation, and show how to transform it and other RKHS Weightings into STAR models. We therefore introduce a new family of STAR models, as well as the means to interpret their outputs in a timely manner.</p>
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<a class="small-title" href="paper_pages/MHJlFCqXdA.html">Is Value Functions Estimation with Classification Plug-and- play for Offline Reinforcement Learning?</a>
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<div class="author-str">Denis Tarasov · Kirill Brilliantov · Dmitrii Kharlapenko</div>
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<p>In deep Reinforcement Learning (RL), value functions are typically approximated using deep neural networks and trained via mean squared error regression objectives to fit the true value functions. Recent research has proposed an alternative approach, utilizing the cross-entropy classification objective, which has demonstrated improved performance and scalability of RL algorithms. However, existing study have not extensively benchmarked the effects of this replacement across various domains, as the primary objective was to demonstrate the efficacy of the concept across a broad spectrum of tasks, without delving into in-depth analysis. Our work seeks to empirically investigate the impact of such a replacement in an offline RL setup and analyze the effects of different aspects on performance. Through large-scale experiments conducted across a diverse range of tasks using different algorithms, we aim to gain deeper insights into the implications of this approach. Our results reveal that incorporating this change can lead to superior performance over state-of-the-art solutions for some algorithms in certain tasks, while maintaining comparable performance levels in other tasks, however for other algorithms this modification might lead to the dramatic performance drop. This findings are crucial for further application of classification approach in research and practical tasks.</p>
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<a class="small-title" href="paper_pages/iVV7IzI55V.html">On Inherent Adversarial Robustness of Active Vision Systems</a>
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<div class="author-str">Amitangshu Mukherjee · Timur Ibrayev · Kaushik Roy</div>
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<p>Deep Neural Networks (DNNs) are susceptible to adversarial inputs, such as imperceptible noise and naturally occurring challenging samples. This vulnerability likely arises from their passive, one-shot processing approach. In contrast, neuroscience suggests that human vision robustly identifies salient object features by actively switching between multiple fixation points (saccades) and processing surroundings with non-uniform resolution (foveation). This information is processed via two pathways: the dorsal (where) and ventral (what) streams, which identify relevant input portions and discard irrelevant details. Building on this perspective, we outline a deep learning-based active dorsal-ventral vision system and adapt two prior methods, FALcon and GFNet, within this framework to evaluate their robustness. We conduct a comprehensive robustness analysis across three categories: adversarially crafted inputs evaluated under transfer attack scenarios, natural adversarial images, and foreground-distorted images. By learning from focused, downsampled glimpses at multiple distinct fixation points, these active methods significantly enhance the robustness of passive networks, achieving a 2-21 % increase in accuracy. This improvement is demonstrated against state-of-the-art transferable black-box attack. On ImageNet-A, a benchmark for naturally occurring hard samples, we show how distinct predictions from multiple fixation points yield performance gains of 1.5-2 times for both CNN and Transformer based networks. Lastly, we qualitatively demonstrate how an active vision system aligns more closely with human perception for structurally distorted images. This alignment leads to more stable and resilient predictions, with lesser catastrophic mispredictions. In contrast, passive methods, which rely on single-shot learning and inference, often lack the necessary structural understanding.</p>
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<a class="small-title" href="paper_pages/QlTLkH6xRC.html">TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis</a>
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<div class="author-str">Sabera J Talukder · Yisong Yue · Georgia Gkioxari</div>
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<p>This work studies the problem of time series analysis with generalist (or foundation) models, which are models trained across many data domains. Drawing inspiration from the widespread success of large language models, we consider the simple strategy of discretely tokenizing time series data drawn from a myriad of datasets via self-supervision, then using the fixed tokenization to solve a variety of tasks across many data domains. Canonically, time series models are either trained on a single dataset or built in a task-specific manner (e.g., a forecasting-only model), where many use patches of time as inputs to the model. As such, performant generalist, discrete representation time series models explored across many tasks are of value. Our method, TOkenized Time Series EMbeddings (TOTEM), produces such generalist time series models with minimal or no fine-tuning while exhibiting strong zero-shot performance. We evaluate TOTEM extensively over nearly 500 experiments on three commonly-studied time series tasks with real-world data: imputation (17 baselines, 12 datasets), anomaly detection (19 baselines, 25 datasets), and forecasting (14 baselines, 12 datasets). We conclude that TOTEM matches or outperforms existing state-of-the-art models in both the canonical specialist setting (i.e., training one model on one domain) as well as the generalist setting (i.e., training a single model on many domains), which demonstrates the efficacy of tokenization for general time series analysis. The open-source implementation is available here: https://github.com/SaberaTalukder/TOTEM; a video summary is available here: https://www.youtube.com/watch?v=OqrCpdb6MJk.</p>
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<a class="small-title" href="paper_pages/lIy0TEUou7.html">Modular Quantization-Aware Training for 6D Object Pose Estimation</a>
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<div class="author-str">Saqib Javed · Chengkun Li · Andrew Lawrence Price · Yinlin Hu · Mathieu Salzmann</div>
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<p>Edge applications, such as collaborative robotics and spacecraft rendezvous, demand efficient 6D object pose estimation on resource-constrained embedded platforms. Existing 6D object pose estimation networks are often too large for such deployments, necessitating compression while maintaining reliable performance. To address this challenge, we introduce Modular Quantization-Aware Training (MQAT), an adaptive and mixed-precision quantization-aware training strategy that exploits the modular structure of modern 6D object pose estimation architectures. MQAT guides a systematic gradated modular quantization sequence and determines module-specific bit precisions, leading to quantized models that outperform those produced by state-of-the-art uniform and mixed-precision quantization techniques. Our experiments showcase the generality of MQAT across datasets, architectures, and quantization algorithms. Additionally, we observe that MQAT quantized models can achieve an accuracy boost (>7% ADI-0.1d) over the baseline full-precision network while reducing model size by a factor of 4x or more.
Project Page: https://saqibjaved1.github.io/MQAT_</p>
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